License
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ITCS.2020.1
URN: urn:nbn:de:0030-drops-116863
URL: https://drops.dagstuhl.de/opus/volltexte/2020/11686/
Go to the corresponding LIPIcs Volume Portal


Goldenberg, Elazar ; Karthik C. S.,

Hardness Amplification of Optimization Problems

pdf-format:
LIPIcs-ITCS-2020-1.pdf (0.5 MB)


Abstract

In this paper, we prove a general hardness amplification scheme for optimization problems based on the technique of direct products. We say that an optimization problem Π is direct product feasible if it is possible to efficiently aggregate any k instances of Π and form one large instance of Π such that given an optimal feasible solution to the larger instance, we can efficiently find optimal feasible solutions to all the k smaller instances. Given a direct product feasible optimization problem Π, our hardness amplification theorem may be informally stated as follows: If there is a distribution D over instances of Π of size n such that every randomized algorithm running in time t(n) fails to solve Π on 1/α(n) fraction of inputs sampled from D, then, assuming some relationships on α(n) and t(n), there is a distribution D' over instances of Π of size O(n⋅α(n)) such that every randomized algorithm running in time t(n)/poly(α(n)) fails to solve Π on 99/100 fraction of inputs sampled from D'. As a consequence of the above theorem, we show hardness amplification of problems in various classes such as NP-hard problems like Max-Clique, Knapsack, and Max-SAT, problems in P such as Longest Common Subsequence, Edit Distance, Matrix Multiplication, and even problems in TFNP such as Factoring and computing Nash equilibrium.

BibTeX - Entry

@InProceedings{goldenberg_et_al:LIPIcs:2020:11686,
  author =	{Elazar Goldenberg and  Karthik C. S.},
  title =	{{Hardness Amplification of Optimization Problems}},
  booktitle =	{11th Innovations in Theoretical Computer Science Conference (ITCS 2020)},
  pages =	{1:1--1:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-134-4},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{151},
  editor =	{Thomas Vidick},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2020/11686},
  URN =		{urn:nbn:de:0030-drops-116863},
  doi =		{10.4230/LIPIcs.ITCS.2020.1},
  annote =	{Keywords: hardness amplification, average case complexity, direct product, optimization problems, fine-grained complexity, TFNP}
}

Keywords: hardness amplification, average case complexity, direct product, optimization problems, fine-grained complexity, TFNP
Seminar: 11th Innovations in Theoretical Computer Science Conference (ITCS 2020)
Issue Date: 2020
Date of publication: 10.01.2020


DROPS-Home | Fulltext Search | Imprint | Privacy Published by LZI